



"""simple docstring"""





import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


lowerCamelCase  			=		logging.getLogger(__name__)
@dataclass
class  lowercase__  (       SCREAMING_SNAKE_CASE ):







       '''simple docstring'''


       UpperCamelCase		=	field(
           default=0.0							,   metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''} )
       UpperCamelCase		=	field(default=SCREAMING_SNAKE_CASE							,   metadata={'''help''': '''Whether to SortishSamler or not.'''} )
       UpperCamelCase		=	field(
           default=SCREAMING_SNAKE_CASE							,   metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''} )
       UpperCamelCase		=	field(default=SCREAMING_SNAKE_CASE							,   metadata={'''help''': '''whether to use adafactor'''} )
       UpperCamelCase		=	field(
           default=SCREAMING_SNAKE_CASE							,   metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''} )
       UpperCamelCase		=	field(
           default=SCREAMING_SNAKE_CASE							,   metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''} )
       UpperCamelCase		=	field(default=SCREAMING_SNAKE_CASE							,   metadata={'''help''': '''Dropout probability. Goes into model.config.'''} )
       UpperCamelCase		=	field(
           default=SCREAMING_SNAKE_CASE							,   metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''} )
       UpperCamelCase		=	field(
           default='''linear'''							,   metadata={'''help''': F"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys() )}"}							,   )






from __future__ import annotations







def 							A__       (    lowerCamelCase    )	->						int:
				UpperCamelCase_:				Optional[Any]						=    len(lowerCamelCase    ) // 2

				# choose the middle 3 elements
				UpperCamelCase_:				Tuple						=    lst[m - 1 : m + 2]

				# if middle element is peak
				if three[1] > three[0] and three[1] > three[2]:
								return three[1]

				# if increasing, recurse on right
				elif three[0] < three[2]:
								if len(lst[:m]    ) == 2:
												m -= 1
								return peak(lst[m:]    )

				# decreasing
				else:
								if len(lst[:m]    ) == 2:
												m += 1
								return peak(lst[:m]    )


if __name__ == "__main__":
							import doctest

							doctest.testmod()






